To effectively balance the load across all nodes in a Spark job processing skewed data, it's crucial to evenly distribute the workload. Configuring spark.default.parallelism to match the number of cores in the cluster ensures that each core is utilized efficiently, maximizing parallelism and evenly spreading the workload. This approach addresses the root cause of performance issues caused by skewed data by ensuring that the workload is distributed in a way that all available resources are used effectively. Other options, such as enabling adaptive skew join optimization or adjusting shuffle partitions, may offer some benefits but are not as directly effective in balancing the load across all nodes as configuring the parallelism to match the cluster's core count.